diff --git a/convert/convert_from_mxnet.py b/convert/convert_from_mxnet.py
new file mode 100644
index 00000000..79d88563
--- /dev/null
+++ b/convert/convert_from_mxnet.py
@@ -0,0 +1,107 @@
+import argparse
+import hashlib
+import os
+
+import mxnet as mx
+import gluoncv
+import torch
+from models.model_factory import create_model
+
+parser = argparse.ArgumentParser(description='Training')
+parser.add_argument('--model', default='all', type=str, metavar='MODEL',
+                    help='Name of model to train (default: "all"')
+
+
+def convert(mxnet_name, torch_name):
+    # download and load the pre-trained model
+    net = gluoncv.model_zoo.get_model(mxnet_name, pretrained=True)
+
+    # create corresponding torch model
+    torch_net = create_model(torch_name)
+
+    mxp = [(k, v) for k, v in net.collect_params().items() if 'running' not in k]
+    torchp = list(torch_net.named_parameters())
+    torch_params = {}
+
+    # convert parameters
+    # NOTE: we are relying on the fact that the order of parameters
+    # are usually exactly the same between these models, thus no key name mapping
+    # is necessary. Asserts will trip if this is not the case.
+    for (tn, tv), (mn, mv) in zip(torchp, mxp):
+        m_split = mn.split('_')
+        t_split = tn.split('.')
+        print(t_split, m_split)
+        print(tv.shape, mv.shape)
+
+        # ensure ordering of BN params match since their sizes are not specific
+        if m_split[-1] == 'gamma':
+            assert t_split[-1] == 'weight'
+        if m_split[-1] == 'beta':
+            assert t_split[-1] == 'bias'
+
+        # ensure shapes match
+        assert all(t == m for t, m in zip(tv.shape, mv.shape))
+
+        torch_tensor = torch.from_numpy(mv.data().asnumpy())
+        torch_params[tn] = torch_tensor
+
+    # convert buffers (batch norm running stats)
+    mxb = [(k, v) for k, v in net.collect_params().items() if any(x in k for x in ['running_mean', 'running_var'])]
+    torchb = [(k, v) for k, v in torch_net.named_buffers() if 'num_batches' not in k]
+    for (tn, tv), (mn, mv) in zip(torchb, mxb):
+        print(tn, mn)
+        print(tv.shape, mv.shape)
+
+        # ensure ordering of BN params match since their sizes are not specific
+        if 'running_var' in tn:
+            assert 'running_var' in mn
+        if 'running_mean' in tn:
+            assert 'running_mean' in mn
+            
+        torch_tensor = torch.from_numpy(mv.data().asnumpy())
+        torch_params[tn] = torch_tensor
+
+    torch_net.load_state_dict(torch_params)
+    torch_filename = './%s.pth' % torch_name
+    torch.save(torch_net.state_dict(), torch_filename)
+    with open(torch_filename, 'rb') as f:
+        sha_hash = hashlib.sha256(f.read()).hexdigest()
+    final_filename = os.path.splitext(torch_filename)[0] + '-' + sha_hash[:8] + '.pth'
+    os.rename(torch_filename, final_filename)
+    print("=> Saved converted model to '{}, SHA256: {}'".format(final_filename, sha_hash))
+
+
+def map_mx_to_torch_model(mx_name):
+    torch_name = mx_name.lower()
+    if torch_name.startswith('se_'):
+        torch_name = torch_name.replace('se_', 'se')
+    elif torch_name.startswith('senet_'):
+        torch_name = torch_name.replace('senet_', 'senet')
+    elif torch_name.startswith('inceptionv3'):
+        torch_name = torch_name.replace('inceptionv3', 'inception_v3')
+    torch_name = 'gluon_' + torch_name
+    return torch_name
+
+
+ALL = ['resnet18_v1b', 'resnet34_v1b', 'resnet50_v1b', 'resnet101_v1b', 'resnet152_v1b',
+       'resnet50_v1c', 'resnet101_v1c', 'resnet152_v1c', 'resnet50_v1d', 'resnet101_v1d', 'resnet152_v1d',
+       #'resnet50_v1e', 'resnet101_v1e', 'resnet152_v1e',
+       'resnet50_v1s', 'resnet101_v1s', 'resnet152_v1s', 'resnext50_32x4d', 'resnext101_32x4d', 'resnext101_64x4d',
+       'se_resnext50_32x4d', 'se_resnext101_32x4d', 'se_resnext101_64x4d', 'senet_154', 'inceptionv3']
+
+
+def main():
+    args = parser.parse_args()
+
+    if not args.model or args.model == 'all':
+        for mx_model in ALL:
+            torch_model = map_mx_to_torch_model(mx_model)
+            convert(mx_model, torch_model)
+    else:
+        mx_model = args.model
+        torch_model = map_mx_to_torch_model(mx_model)
+        convert(mx_model, torch_model)
+
+
+if __name__ == '__main__':
+    main()
diff --git a/models/gluon_resnet.py b/models/gluon_resnet.py
new file mode 100644
index 00000000..9918b3e5
--- /dev/null
+++ b/models/gluon_resnet.py
@@ -0,0 +1,680 @@
+"""Pytorch ResNet implementation w/ tweaks
+This file is a copy of https://github.com/pytorch/vision 'resnet.py' (BSD-3-Clause) with
+additional dropout and dynamic global avg/max pool.
+
+ResNext additions added by Ross Wightman
+"""
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import math
+from models.helpers import load_pretrained
+from models.adaptive_avgmax_pool import SelectAdaptivePool2d
+from data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
+
+__all__ = ['GluonResNet', 'gluon_resnet18_v1b', 'gluon_resnet34_v1b', 'gluon_resnet50_v1b', 'gluon_resnet101_v1b',
+    'gluon_resnet152_v1b', 'gluon_resnet50_v1c', 'gluon_resnet101_v1c', 'gluon_resnet152_v1c', 'gluon_resnet50_v1d',
+    'gluon_resnet101_v1d', 'gluon_resnet152_v1d', 'gluon_resnet50_v1e', 'gluon_resnet101_v1e', 'gluon_resnet152_v1e',
+    'gluon_resnet50_v1s', 'gluon_resnet101_v1s', 'gluon_resnet152_v1s', 'gluon_resnext50_32x4d',
+    'gluon_resnext101_32x4d', 'gluon_resnext101_64x4d', 'gluon_resnext152_32x4d', 'gluon_seresnext50_32x4d',
+    'gluon_seresnext101_32x4d', 'gluon_seresnext101_64x4d', 'gluon_seresnext152_32x4d', 'gluon_senet154'
+]
+
+
+def _cfg(url='', **kwargs):
+    return {
+        'url': url,
+        'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
+        'crop_pct': 0.875, 'interpolation': 'bicubic',
+        'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
+        'first_conv': 'conv1', 'classifier': 'fc',
+        **kwargs
+    }
+
+
+default_cfgs = {
+    'gluon_resnet18_v1b': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet18_v1b-0757602b.pth'),
+    'gluon_resnet34_v1b': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet34_v1b-c6d82d59.pth'),
+    'gluon_resnet50_v1b': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet50_v1b-0ebe02e2.pth'),
+    'gluon_resnet101_v1b': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet101_v1b-3b017079.pth'),
+    'gluon_resnet152_v1b': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet152_v1b-c1edb0dd.pth'),
+    'gluon_resnet50_v1c': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet50_v1c-48092f55.pth'),
+    'gluon_resnet101_v1c': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet101_v1c-1f26822a.pth'),
+    'gluon_resnet152_v1c': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet152_v1c-a3bb0b98.pth'),
+    'gluon_resnet50_v1d': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet50_v1d-818a1b1b.pth'),
+    'gluon_resnet101_v1d': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet101_v1d-0f9c8644.pth'),
+    'gluon_resnet152_v1d': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet152_v1d-bd354e12.pth'),
+    'gluon_resnet50_v1e': _cfg(url=''),
+    'gluon_resnet101_v1e': _cfg(url=''),
+    'gluon_resnet152_v1e': _cfg(url=''),
+    'gluon_resnet50_v1s': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet50_v1s-1762acc0.pth'),
+    'gluon_resnet101_v1s': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet101_v1s-60fe0cc1.pth'),
+    'gluon_resnet152_v1s': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet152_v1s-dcc41b81.pth'),
+    'gluon_resnext50_32x4d': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnext50_32x4d-e6a097c1.pth'),
+    'gluon_resnext101_32x4d': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnext101_32x4d-b253c8c4.pth'),
+    'gluon_resnext101_64x4d': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnext101_64x4d-f9a8e184.pth'),
+    'gluon_resnext152_32x4d': _cfg(url=''),
+    'gluon_seresnext50_32x4d': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_seresnext50_32x4d-90cf2d6e.pth'),
+    'gluon_seresnext101_32x4d': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_seresnext101_32x4d-cf52900d.pth'),
+    'gluon_seresnext101_64x4d': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_seresnext101_64x4d-f9926f93.pth'),
+    'gluon_seresnext152_32x4d': _cfg(url=''),
+    'gluon_senet154': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_senet154-70a1a3c0.pth'),
+}
+
+
+def _get_padding(kernel_size, stride, dilation=1):
+    padding = ((stride - 1) + dilation * (kernel_size - 1)) // 2
+    return padding
+
+
+class SEModule(nn.Module):
+
+    def __init__(self, channels, reduction_channels):
+        super(SEModule, self).__init__()
+        #self.avg_pool = nn.AdaptiveAvgPool2d(1)
+        self.fc1 = nn.Conv2d(
+            channels, reduction_channels, kernel_size=1, padding=0, bias=True)
+        self.relu = nn.ReLU()
+        self.fc2 = nn.Conv2d(
+            reduction_channels, channels, kernel_size=1, padding=0, bias=True)
+        self.sigmoid = nn.Sigmoid()
+
+    def forward(self, x):
+        module_input = x
+        #x = self.avg_pool(x)
+        x = x.view(x.size(0), x.size(1), -1).mean(-1).view(x.size(0), x.size(1), 1, 1)
+        x = self.fc1(x)
+        x = self.relu(x)
+        x = self.fc2(x)
+        x = self.sigmoid(x)
+        return module_input * x
+
+
+class BasicBlockGl(nn.Module):
+    expansion = 1
+
+    def __init__(self, inplanes, planes, stride=1, downsample=None,
+                 cardinality=1, base_width=64, use_se=False,
+                 reduce_first=1, dilation=1, previous_dilation=1, norm_layer=nn.BatchNorm2d):
+        super(BasicBlockGl, self).__init__()
+
+        assert cardinality == 1, 'BasicBlock only supports cardinality of 1'
+        assert base_width == 64, 'BasicBlock doest not support changing base width'
+        first_planes = planes // reduce_first
+        outplanes = planes * self.expansion
+
+        self.conv1 = nn.Conv2d(
+            inplanes, first_planes, kernel_size=3, stride=stride, padding=dilation,
+            dilation=dilation, bias=False)
+        self.bn1 = norm_layer(first_planes)
+        self.relu = nn.ReLU()
+        self.conv2 = nn.Conv2d(
+            first_planes, outplanes, kernel_size=3, padding=previous_dilation,
+            dilation=previous_dilation, bias=False)
+        self.bn2 = norm_layer(outplanes)
+        self.se = SEModule(outplanes, planes // 4) if use_se else None
+        self.downsample = downsample
+        self.stride = stride
+        self.dilation = dilation
+
+    def forward(self, x):
+        residual = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+        out = self.conv2(out)
+        out = self.bn2(out)
+
+        if self.se is not None:
+            out = self.se(out)
+
+        if self.downsample is not None:
+            residual = self.downsample(x)
+
+        out += residual
+        out = self.relu(out)
+
+        return out
+
+
+class BottleneckGl(nn.Module):
+    expansion = 4
+
+    def __init__(self, inplanes, planes, stride=1, downsample=None,
+                 cardinality=1, base_width=64, use_se=False,
+                 reduce_first=1, dilation=1, previous_dilation=1, norm_layer=nn.BatchNorm2d):
+        super(BottleneckGl, self).__init__()
+
+        width = int(math.floor(planes * (base_width / 64)) * cardinality)
+        first_planes = width // reduce_first
+        outplanes = planes * self.expansion
+
+        self.conv1 = nn.Conv2d(inplanes, first_planes, kernel_size=1, bias=False)
+        self.bn1 = norm_layer(first_planes)
+        self.conv2 = nn.Conv2d(
+            first_planes, width, kernel_size=3, stride=stride,
+            padding=dilation, dilation=dilation, groups=cardinality, bias=False)
+        self.bn2 = norm_layer(width)
+        self.conv3 = nn.Conv2d(width, outplanes, kernel_size=1, bias=False)
+        self.bn3 = norm_layer(outplanes)
+        self.se = SEModule(outplanes, planes // 4) if use_se else None
+        self.relu = nn.ReLU()
+        self.downsample = downsample
+        self.stride = stride
+        self.dilation = dilation
+
+    def forward(self, x):
+        residual = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+        out = self.relu(out)
+
+        out = self.conv3(out)
+        out = self.bn3(out)
+
+        if self.se is not None:
+            out = self.se(out)
+
+        if self.downsample is not None:
+            residual = self.downsample(x)
+
+        out += residual
+        out = self.relu(out)
+
+        return out
+
+
+class GluonResNet(nn.Module):
+    """ Gluon ResNet (https://gluon-cv.mxnet.io/model_zoo/classification.html)
+    This class implements all variants of ResNet, ResNeXt, SE-ResNeXt, and SENet found in the gluon model zoo that
+      * have stride in 3x3 conv layer of bottleneck
+      * have conv-bn-act ordering
+
+    Included ResNet variants are:
+      * v1b - 7x7 stem, stem_width=64, same as torchvision ResNet (checkpoint compatible), or NVIDIA ResNet 'v1.5'
+      * v1c - 3 layer deep 3x3 stem, stem_width = 32
+      * v1d - 3 layer deep 3x3 stem, stem_width = 32, average pool in downsample
+      * v1e - 3 layer deep 3x3 stem, stem_width = 64, average pool in downsample  *no pretrained weights available
+      * v1s - 3 layer deep 3x3 stem, stem_width = 64
+
+    ResNeXt is standard and checkpoint compatible with torchvision pretrained models. 7x7 stem,
+        stem_width = 64, standard cardinality and base width calcs
+
+    SE-ResNeXt is standard. 7x7 stem, stem_width = 64,
+        checkpoints are not compatible with Cadene pretrained, but could be with key mapping
+
+    SENet-154 is standard. 3 layer deep 3x3 stem (same as v1c-v1s), stem_width = 64, cardinality=64,
+        reduction by 2 on width of first bottleneck convolution, 3x3 downsample convs after first block
+
+    Original ResNet-V1, ResNet-V2 (bn-act-conv), and SE-ResNet (stride in first bottleneck conv) are NOT supported.
+    They do have Gluon pretrained weights but are, at best, comparable (or inferior) to the supported models.
+
+    Parameters
+    ----------
+    block : Block
+        Class for the residual block. Options are BasicBlockGl, BottleneckGl.
+    layers : list of int
+        Numbers of layers in each block
+    num_classes : int, default 1000
+        Number of classification classes.
+    deep_stem : bool, default False
+        Whether to replace the 7x7 conv1 with 3 3x3 convolution layers.
+    block_reduce_first: int, default 1
+        Reduction factor for first convolution output width of residual blocks,
+        1 for all archs except senets, where 2
+    down_kernel_size: int, default 1
+        Kernel size of residual block downsampling path, 1x1 for most archs, 3x3 for senets
+    avg_down : bool, default False
+        Whether to use average pooling for projection skip connection between stages/downsample.
+    dilated : bool, default False
+        Applying dilation strategy to pretrained ResNet yielding a stride-8 model,
+        typically used in Semantic Segmentation.
+    """
+    def __init__(self, block, layers, num_classes=1000, in_chans=3, use_se=False,
+                 cardinality=1, base_width=64, stem_width=64, deep_stem=False,
+                 block_reduce_first=1, down_kernel_size=1, avg_down=False, dilated=False,
+                 norm_layer=nn.BatchNorm2d, drop_rate=0.0, global_pool='avg'):
+        self.num_classes = num_classes
+        self.inplanes = stem_width * 2 if deep_stem else 64
+        self.cardinality = cardinality
+        self.base_width = base_width
+        self.drop_rate = drop_rate
+        self.expansion = block.expansion
+        self.dilated = dilated
+        super(GluonResNet, self).__init__()
+
+        if not deep_stem:
+            self.conv1 = nn.Conv2d(in_chans, stem_width, kernel_size=7, stride=2, padding=3, bias=False)
+        else:
+            conv1_modules = [
+                nn.Conv2d(in_chans, stem_width, 3, stride=2, padding=1, bias=False),
+                norm_layer(stem_width),
+                nn.ReLU(),
+                nn.Conv2d(stem_width, stem_width, 3, stride=1, padding=1, bias=False),
+                norm_layer(stem_width),
+                nn.ReLU(),
+                nn.Conv2d(stem_width, self.inplanes, 3, stride=1, padding=1, bias=False),
+            ]
+            self.conv1 = nn.Sequential(*conv1_modules)
+        self.bn1 = norm_layer(self.inplanes)
+        self.relu = nn.ReLU()
+        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+        stride_3_4 = 1 if self.dilated else 2
+        dilation_3 = 2 if self.dilated else 1
+        dilation_4 = 4 if self.dilated else 1
+        self.layer1 = self._make_layer(
+            block, 64, layers[0], stride=1, reduce_first=block_reduce_first,
+            use_se=use_se, avg_down=avg_down, down_kernel_size=1, norm_layer=norm_layer)
+        self.layer2 = self._make_layer(
+            block, 128, layers[1], stride=2, reduce_first=block_reduce_first,
+            use_se=use_se, avg_down=avg_down, down_kernel_size=down_kernel_size, norm_layer=norm_layer)
+        self.layer3 = self._make_layer(
+            block, 256, layers[2], stride=stride_3_4, dilation=dilation_3, reduce_first=block_reduce_first,
+            use_se=use_se, avg_down=avg_down, down_kernel_size=down_kernel_size, norm_layer=norm_layer)
+        self.layer4 = self._make_layer(
+            block, 512, layers[3], stride=stride_3_4, dilation=dilation_4, reduce_first=block_reduce_first,
+            use_se=use_se, avg_down=avg_down, down_kernel_size=down_kernel_size, norm_layer=norm_layer)
+        self.global_pool = SelectAdaptivePool2d(pool_type=global_pool)
+        self.num_features = 512 * block.expansion
+        self.fc = nn.Linear(self.num_features * self.global_pool.feat_mult(), num_classes)
+
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
+            elif isinstance(m, nn.BatchNorm2d):
+                nn.init.constant_(m.weight, 1.)
+                nn.init.constant_(m.bias, 0.)
+
+    def _make_layer(self, block, planes, blocks, stride=1, dilation=1, reduce_first=1,
+                    use_se=False, avg_down=False, down_kernel_size=1, norm_layer=nn.BatchNorm2d):
+        downsample = None
+        if stride != 1 or self.inplanes != planes * block.expansion:
+            downsample_padding = _get_padding(down_kernel_size, stride)
+            if avg_down:
+                avg_stride = stride if dilation == 1 else 1
+                downsample_layers = [
+                    nn.AvgPool2d(avg_stride, avg_stride, ceil_mode=True, count_include_pad=False),
+                    nn.Conv2d(self.inplanes, planes * block.expansion, down_kernel_size,
+                              stride=1, padding=downsample_padding, bias=False),
+                    norm_layer(planes * block.expansion),
+                ]
+            else:
+                downsample_layers = [
+                    nn.Conv2d(self.inplanes, planes * block.expansion, down_kernel_size,
+                              stride=stride, padding=downsample_padding, bias=False),
+                    norm_layer(planes * block.expansion),
+                ]
+            downsample = nn.Sequential(*downsample_layers)
+
+        first_dilation = 1 if dilation in (1, 2) else 2
+        layers = [block(
+            self.inplanes, planes, stride, downsample,
+            cardinality=self.cardinality, base_width=self.base_width, reduce_first=reduce_first,
+            use_se=use_se, dilation=first_dilation, previous_dilation=dilation, norm_layer=norm_layer)]
+        self.inplanes = planes * block.expansion
+        for i in range(1, blocks):
+            layers.append(block(
+                self.inplanes, planes,
+                cardinality=self.cardinality, base_width=self.base_width, reduce_first=reduce_first,
+                use_se=use_se, dilation=dilation, previous_dilation=dilation, norm_layer=norm_layer))
+
+        return nn.Sequential(*layers)
+
+    def get_classifier(self):
+        return self.fc
+
+    def reset_classifier(self, num_classes, global_pool='avg'):
+        self.global_pool = SelectAdaptivePool2d(pool_type=global_pool)
+        self.num_classes = num_classes
+        del self.fc
+        if num_classes:
+            self.fc = nn.Linear(self.num_features * self.global_pool.feat_mult(), num_classes)
+        else:
+            self.fc = None
+
+    def forward_features(self, x, pool=True):
+        x = self.conv1(x)
+        x = self.bn1(x)
+        x = self.relu(x)
+        x = self.maxpool(x)
+
+        x = self.layer1(x)
+        x = self.layer2(x)
+        x = self.layer3(x)
+        x = self.layer4(x)
+
+        if pool:
+            x = self.global_pool(x)
+            x = x.view(x.size(0), -1)
+        return x
+
+    def forward(self, x):
+        x = self.forward_features(x)
+        if self.drop_rate > 0.:
+            x = F.dropout(x, p=self.drop_rate, training=self.training)
+        x = self.fc(x)
+        return x
+
+
+def gluon_resnet18_v1b(num_classes=1000, in_chans=3, pretrained=False, **kwargs):
+    """Constructs a ResNet-18 model.
+    """
+    default_cfg = default_cfgs['gluon_resnet18_v1b']
+    model = GluonResNet(BasicBlockGl, [2, 2, 2, 2], num_classes=num_classes, in_chans=in_chans, **kwargs)
+    model.default_cfg = default_cfg
+    if pretrained:
+        load_pretrained(model, default_cfg, num_classes, in_chans)
+    return model
+
+
+def gluon_resnet34_v1b(num_classes=1000, in_chans=3, pretrained=False, **kwargs):
+    """Constructs a ResNet-34 model.
+    """
+    default_cfg = default_cfgs['gluon_resnet34_v1b']
+    model = GluonResNet(BasicBlockGl, [3, 4, 6, 3], num_classes=num_classes, in_chans=in_chans, **kwargs)
+    model.default_cfg = default_cfg
+    if pretrained:
+        load_pretrained(model, default_cfg, num_classes, in_chans)
+    return model
+
+
+def gluon_resnet50_v1b(num_classes=1000, in_chans=3, pretrained=False, **kwargs):
+    """Constructs a ResNet-50 model.
+    """
+    default_cfg = default_cfgs['gluon_resnet50_v1b']
+    model = GluonResNet(BottleneckGl, [3, 4, 6, 3], num_classes=num_classes, in_chans=in_chans, **kwargs)
+    model.default_cfg = default_cfg
+    if pretrained:
+        load_pretrained(model, default_cfg, num_classes, in_chans)
+    return model
+
+
+def gluon_resnet101_v1b(num_classes=1000, in_chans=3, pretrained=False, **kwargs):
+    """Constructs a ResNet-101 model.
+    """
+    default_cfg = default_cfgs['gluon_resnet101_v1b']
+    model = GluonResNet(BottleneckGl, [3, 4, 23, 3], num_classes=num_classes, in_chans=in_chans, **kwargs)
+    model.default_cfg = default_cfg
+    if pretrained:
+        load_pretrained(model, default_cfg, num_classes, in_chans)
+    return model
+
+
+def gluon_resnet152_v1b(num_classes=1000, in_chans=3, pretrained=False, **kwargs):
+    """Constructs a ResNet-152 model.
+    """
+    default_cfg = default_cfgs['gluon_resnet152_v1b']
+    model = GluonResNet(BottleneckGl, [3, 8, 36, 3], num_classes=num_classes, in_chans=in_chans, **kwargs)
+    model.default_cfg = default_cfg
+    if pretrained:
+        load_pretrained(model, default_cfg, num_classes, in_chans)
+    return model
+
+
+def gluon_resnet50_v1c(num_classes=1000, in_chans=3, pretrained=False, **kwargs):
+    """Constructs a ResNet-50 model.
+    """
+    default_cfg = default_cfgs['gluon_resnet50_v1c']
+    model = GluonResNet(BottleneckGl, [3, 4, 6, 3], num_classes=num_classes, in_chans=in_chans,
+                        stem_width=32, deep_stem=True, **kwargs)
+    model.default_cfg = default_cfg
+    if pretrained:
+        load_pretrained(model, default_cfg, num_classes, in_chans)
+    return model
+
+
+def gluon_resnet101_v1c(num_classes=1000, in_chans=3, pretrained=False, **kwargs):
+    """Constructs a ResNet-101 model.
+    """
+    default_cfg = default_cfgs['gluon_resnet101_v1c']
+    model = GluonResNet(BottleneckGl, [3, 4, 23, 3], num_classes=num_classes, in_chans=in_chans,
+                        stem_width=32, deep_stem=True, **kwargs)
+    model.default_cfg = default_cfg
+    if pretrained:
+        load_pretrained(model, default_cfg, num_classes, in_chans)
+    return model
+
+
+def gluon_resnet152_v1c(num_classes=1000, in_chans=3, pretrained=False, **kwargs):
+    """Constructs a ResNet-152 model.
+    """
+    default_cfg = default_cfgs['gluon_resnet152_v1c']
+    model = GluonResNet(BottleneckGl, [3, 8, 36, 3], num_classes=num_classes, in_chans=in_chans,
+                        stem_width=32, deep_stem=True, **kwargs)
+    model.default_cfg = default_cfg
+    if pretrained:
+        load_pretrained(model, default_cfg, num_classes, in_chans)
+    return model
+
+
+def gluon_resnet50_v1d(num_classes=1000, in_chans=3, pretrained=False, **kwargs):
+    """Constructs a ResNet-50 model.
+    """
+    default_cfg = default_cfgs['gluon_resnet50_v1d']
+    model = GluonResNet(BottleneckGl, [3, 4, 6, 3], num_classes=num_classes, in_chans=in_chans,
+                        stem_width=32, deep_stem=True, avg_down=True, **kwargs)
+    model.default_cfg = default_cfg
+    if pretrained:
+        load_pretrained(model, default_cfg, num_classes, in_chans)
+    return model
+
+
+def gluon_resnet101_v1d(num_classes=1000, in_chans=3, pretrained=False, **kwargs):
+    """Constructs a ResNet-101 model.
+    """
+    default_cfg = default_cfgs['gluon_resnet101_v1d']
+    model = GluonResNet(BottleneckGl, [3, 4, 23, 3], num_classes=num_classes, in_chans=in_chans,
+                        stem_width=32, deep_stem=True, avg_down=True, **kwargs)
+    model.default_cfg = default_cfg
+    if pretrained:
+        load_pretrained(model, default_cfg, num_classes, in_chans)
+    return model
+
+
+def gluon_resnet152_v1d(num_classes=1000, in_chans=3, pretrained=False, **kwargs):
+    """Constructs a ResNet-152 model.
+    """
+    default_cfg = default_cfgs['gluon_resnet152_v1d']
+    model = GluonResNet(BottleneckGl, [3, 8, 36, 3], num_classes=num_classes, in_chans=in_chans,
+                        stem_width=32, deep_stem=True, avg_down=True, **kwargs)
+    model.default_cfg = default_cfg
+    if pretrained:
+        load_pretrained(model, default_cfg, num_classes, in_chans)
+    return model
+
+
+def gluon_resnet50_v1e(num_classes=1000, in_chans=3, pretrained=False, **kwargs):
+    """Constructs a ResNet-50-V1e model. No pretrained weights for any 'e' variants
+    """
+    default_cfg = default_cfgs['gluon_resnet50_v1e']
+    model = GluonResNet(BottleneckGl, [3, 4, 6, 3], num_classes=num_classes, in_chans=in_chans,
+                        stem_width=64, deep_stem=True, avg_down=True, **kwargs)
+    model.default_cfg = default_cfg
+    #if pretrained:
+    #    load_pretrained(model, default_cfg, num_classes, in_chans)
+    return model
+
+
+def gluon_resnet101_v1e(num_classes=1000, in_chans=3, pretrained=False, **kwargs):
+    """Constructs a ResNet-101 model.
+    """
+    default_cfg = default_cfgs['gluon_resnet101_v1e']
+    model = GluonResNet(BottleneckGl, [3, 4, 23, 3], num_classes=num_classes, in_chans=in_chans,
+                        stem_width=64, deep_stem=True, avg_down=True, **kwargs)
+    model.default_cfg = default_cfg
+    if pretrained:
+        load_pretrained(model, default_cfg, num_classes, in_chans)
+    return model
+
+
+def gluon_resnet152_v1e(num_classes=1000, in_chans=3, pretrained=False, **kwargs):
+    """Constructs a ResNet-152 model.
+    """
+    default_cfg = default_cfgs['gluon_resnet152_v1e']
+    model = GluonResNet(BottleneckGl, [3, 8, 36, 3], num_classes=num_classes, in_chans=in_chans,
+                        stem_width=64, deep_stem=True, avg_down=True, **kwargs)
+    model.default_cfg = default_cfg
+    if pretrained:
+        load_pretrained(model, default_cfg, num_classes, in_chans)
+    return model
+
+
+def gluon_resnet50_v1s(num_classes=1000, in_chans=3, pretrained=False, **kwargs):
+    """Constructs a ResNet-50 model.
+    """
+    default_cfg = default_cfgs['gluon_resnet50_v1s']
+    model = GluonResNet(BottleneckGl, [3, 4, 6, 3], num_classes=num_classes, in_chans=in_chans,
+                        stem_width=64, deep_stem=True, **kwargs)
+    model.default_cfg = default_cfg
+    if pretrained:
+        load_pretrained(model, default_cfg, num_classes, in_chans)
+    return model
+
+
+def gluon_resnet101_v1s(num_classes=1000, in_chans=3, pretrained=False, **kwargs):
+    """Constructs a ResNet-101 model.
+    """
+    default_cfg = default_cfgs['gluon_resnet101_v1s']
+    model = GluonResNet(BottleneckGl, [3, 4, 23, 3], num_classes=num_classes, in_chans=in_chans,
+                        stem_width=64, deep_stem=True, **kwargs)
+    model.default_cfg = default_cfg
+    if pretrained:
+        load_pretrained(model, default_cfg, num_classes, in_chans)
+    return model
+
+
+def gluon_resnet152_v1s(num_classes=1000, in_chans=3, pretrained=False, **kwargs):
+    """Constructs a ResNet-152 model.
+    """
+    default_cfg = default_cfgs['gluon_resnet152_v1s']
+    model = GluonResNet(BottleneckGl, [3, 8, 36, 3], num_classes=num_classes, in_chans=in_chans,
+                        stem_width=64, deep_stem=True, **kwargs)
+    model.default_cfg = default_cfg
+    if pretrained:
+        load_pretrained(model, default_cfg, num_classes, in_chans)
+    return model
+
+
+def gluon_resnext50_32x4d(num_classes=1000, in_chans=3, pretrained=False, **kwargs):
+    """Constructs a ResNeXt50-32x4d model.
+    """
+    default_cfg = default_cfgs['gluon_resnext50_32x4d']
+    model = GluonResNet(
+        BottleneckGl, [3, 4, 6, 3], cardinality=32, base_width=4,
+        num_classes=num_classes, in_chans=in_chans, **kwargs)
+    model.default_cfg = default_cfg
+    if pretrained:
+        load_pretrained(model, default_cfg, num_classes, in_chans)
+    return model
+
+
+def gluon_resnext101_32x4d(num_classes=1000, in_chans=3, pretrained=False, **kwargs):
+    """Constructs a ResNeXt-101 model.
+    """
+    default_cfg = default_cfgs['gluon_resnext101_32x4d']
+    model = GluonResNet(
+        BottleneckGl, [3, 4, 23, 3], cardinality=32, base_width=4,
+        num_classes=num_classes, in_chans=in_chans, **kwargs)
+    model.default_cfg = default_cfg
+    if pretrained:
+        load_pretrained(model, default_cfg, num_classes, in_chans)
+    return model
+
+
+def gluon_resnext101_64x4d(num_classes=1000, in_chans=3, pretrained=False, **kwargs):
+    """Constructs a ResNeXt-101 model.
+    """
+    default_cfg = default_cfgs['gluon_resnext101_64x4d']
+    model = GluonResNet(
+        BottleneckGl, [3, 4, 23, 3], cardinality=64, base_width=4,
+        num_classes=num_classes, in_chans=in_chans, **kwargs)
+    model.default_cfg = default_cfg
+    if pretrained:
+        load_pretrained(model, default_cfg, num_classes, in_chans)
+    return model
+
+
+def gluon_resnext152_32x4d(num_classes=1000, in_chans=3, pretrained=False, **kwargs):
+    """Constructs a ResNeXt152-32x4d model.
+    """
+    default_cfg = default_cfgs['gluon_resnext152_32x4d']
+    model = GluonResNet(
+        BottleneckGl, [3, 8, 36, 3], cardinality=32, base_width=4,
+        num_classes=num_classes, in_chans=in_chans, **kwargs)
+    model.default_cfg = default_cfg
+    if pretrained:
+        load_pretrained(model, default_cfg, num_classes, in_chans)
+    return model
+
+
+def gluon_seresnext50_32x4d(num_classes=1000, in_chans=3, pretrained=False, **kwargs):
+    """Constructs a SEResNeXt50-32x4d model.
+    """
+    default_cfg = default_cfgs['gluon_seresnext50_32x4d']
+    model = GluonResNet(
+        BottleneckGl, [3, 4, 6, 3], cardinality=32, base_width=4, use_se=True,
+        num_classes=num_classes, in_chans=in_chans, **kwargs)
+    model.default_cfg = default_cfg
+    if pretrained:
+        load_pretrained(model, default_cfg, num_classes, in_chans)
+    return model
+
+
+def gluon_seresnext101_32x4d(num_classes=1000, in_chans=3, pretrained=False, **kwargs):
+    """Constructs a SEResNeXt-101-32x4d model.
+    """
+    default_cfg = default_cfgs['gluon_seresnext101_32x4d']
+    model = GluonResNet(
+        BottleneckGl, [3, 4, 23, 3], cardinality=32, base_width=4, use_se=True,
+        num_classes=num_classes, in_chans=in_chans, **kwargs)
+    model.default_cfg = default_cfg
+    if pretrained:
+        load_pretrained(model, default_cfg, num_classes, in_chans)
+    return model
+
+
+def gluon_seresnext101_64x4d(num_classes=1000, in_chans=3, pretrained=False, **kwargs):
+    """Constructs a SEResNeXt-101-64x4d model.
+    """
+    default_cfg = default_cfgs['gluon_seresnext101_64x4d']
+    model = GluonResNet(
+        BottleneckGl, [3, 4, 23, 3], cardinality=64, base_width=4, use_se=True,
+        num_classes=num_classes, in_chans=in_chans, **kwargs)
+    model.default_cfg = default_cfg
+    if pretrained:
+        load_pretrained(model, default_cfg, num_classes, in_chans)
+    return model
+
+
+def gluon_seresnext152_32x4d(num_classes=1000, in_chans=3, pretrained=False, **kwargs):
+    """Constructs a SEResNeXt152-32x4d model.
+    """
+    default_cfg = default_cfgs['gluon_seresnext152_32x4d']
+    model = GluonResNet(
+        BottleneckGl, [3, 8, 36, 3], cardinality=32, base_width=4, use_se=True,
+        num_classes=num_classes, in_chans=in_chans, **kwargs)
+    model.default_cfg = default_cfg
+    #if pretrained:
+    #    load_pretrained(model, default_cfg, num_classes, in_chans)
+    return model
+
+
+def gluon_senet154(num_classes=1000, in_chans=3, pretrained=False, **kwargs):
+    """Constructs an SENet-154 model.
+    """
+    default_cfg = default_cfgs['gluon_senet154']
+    model = GluonResNet(
+        BottleneckGl, [3, 8, 36, 3], cardinality=64, base_width=4, use_se=True,
+        deep_stem=True, down_kernel_size=3, block_reduce_first=2,
+        num_classes=num_classes, in_chans=in_chans, **kwargs)
+    model.default_cfg = default_cfg
+    if pretrained:
+        load_pretrained(model, default_cfg, num_classes, in_chans)
+    return model
+
diff --git a/models/model_factory.py b/models/model_factory.py
index ca108274..4da37c04 100644
--- a/models/model_factory.py
+++ b/models/model_factory.py
@@ -14,6 +14,14 @@ from models.genmobilenet import \
     mobilenetv1_100, mobilenetv2_100, mobilenetv3_050, mobilenetv3_075, mobilenetv3_100,\
     fbnetc_100, chamnetv1_100, chamnetv2_100, spnasnet_100
 from models.inception_v3 import inception_v3, gluon_inception_v3, tf_inception_v3, adv_inception_v3
+from models.gluon_resnet import gluon_resnet18_v1b, gluon_resnet34_v1b, gluon_resnet50_v1b, gluon_resnet101_v1b, \
+    gluon_resnet152_v1b, gluon_resnet50_v1c, gluon_resnet101_v1c, gluon_resnet152_v1c, \
+    gluon_resnet50_v1d, gluon_resnet101_v1d, gluon_resnet152_v1d, \
+    gluon_resnet50_v1e, gluon_resnet101_v1e, gluon_resnet152_v1e, \
+    gluon_resnet50_v1s, gluon_resnet101_v1s, gluon_resnet152_v1s, \
+    gluon_resnext50_32x4d, gluon_resnext101_32x4d , gluon_resnext101_64x4d, gluon_resnext152_32x4d, \
+    gluon_seresnext50_32x4d, gluon_seresnext101_32x4d, gluon_seresnext101_64x4d, gluon_seresnext152_32x4d, \
+    gluon_senet154
 
 from models.helpers import load_checkpoint